This project demonstrates a machine learning model made using Teachable Machine, a tool that let's anyone make machine learning models without writing any code.
We trained a model that determines banana ripeness, but you can train your own model to do anything you want! Read more about how to train an image model in our blog post.
If you want to remix this project there are a few ways to get started:
If you train your own Teachable Machine model, when you export the model you will get a URL for it. In the index.html file,
if you set the URL variable equal to your new model, the site will load your classes and run your model.
Everytime the model makes a prediction, we have the bar graph update using the prediction. We tell the model to do this when we call
setupModel in the index.html file. The second parameter to the setupModel function is a callback that takes the prediction data
from the model and does something with it. If you want someything else to happen using the prediction data, feel free to modify the
callback function:
setupModel(URL, data => {
updateBarGraph(data);
// Do more with the prediction data here.
});
The callback function takes in a data parameter. This data parameter is an array of objects that store a className and a probability.
An example of prediction data returned by the model to use in your callback might look like this:
data = [
{ className: 'Class 1', probability: .25 },
{ className: 'Class 2', probability: .75 },
]
This project uses the tmImage library. To learn more about this library,
how to use it, read the documentation.
If you make something cool on glitch with Teachable Machine, send us your project at teachablemachine-support@google.com so we can add it to our collection!, or share your project on twitter using #teachablemachine so we can check it out!